2023-01-27T08:07:02Z
https://meral.edu.mm/oai
oai:meral.edu.mm:recid/4442
2021-12-13T00:46:23Z
1582963302567:1597824273898
user-ucsy
An Improved Ant Colony System Based on Dynamic Candidate Set and Entropy for Traveling Salesman Problem
Hlaing, Zar Chi Su Su
Khine, May Aye
The Ant Colony Optimization (ACO) is ametaheuristic algorithm used for combinatorialoptimization problems. It is a good choice formany hard combinatorial problems because it ismore efficient and produces better solutions thangreedy algorithms. However, ACO iscomputationally expensive and it can still trap inlocal optima, take a long time to compute asolution on large problem sets and prematureconvergence problem. The main idea of themodification is to limit the number of elementschoices to a sensible subset, or candidate list,which can limit the selection scope of ants ateach step and thus substantially reduce the sizeof search space and to measure the uncertaintyof the path selection and evolution by using theinformation entropy self-adaptively. Simulationstudy and performance comparison on TravelingSalesman Problem show that the improvedalgorithm can converge at global optimum witha high probability. It also shows a fasterconvergence to the solutions than the standardalgorithm.
2012-02-28
http://hdl.handle.net/20.500.12678/0000004442
https://meral.edu.mm/records/4442